Systems and methods for using cell granularitry in evaluating immune response to infection

ABSTRACT

Systems and methods for characterizing immune response to infection using cellular analysis, such as a hematological cellular analyzer. In some instances, the immune response may be characterized as normal or abnormal based on one or more blood cell population parameters. In some instances, abnormal characterization may be used to identify patients with sepsis or at elevated risk of developing sepsis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is related to, and claims the benefit of, provisional patentapplication 62/873,575 titled “systems and methods for evaluating immuneresponse to infection”, filed in the United States Patent Office on Jul.12, 2019.

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulatedhost response to infection. Sepsis is a global healthcare crisis,affecting over 30 million people worldwide each year. The occurrence ofsepsis is increasing at an annual rate of 1.5%, making it a significantglobal healthcare concern. Sepsis has a high mortality rate, killingmore individuals than prostate cancer, breast cancer and HIV/AIDScombined.

In addition to the human toll, sepsis is costly to healthcareorganizations. Sepsis-related costs—which may include longer hospitalstays, ICU admissions, hospital readmissions, and extensive testing andpatient monitoring—surpass $24 billion.

Sepsis is a syndrome defined by a set of signs and symptoms. Althoughsepsis is associated with infection, there is no single cause of sepsis,which can arise from bacterial, viral or fungal infections. Of course,not all infections result in sepsis, and the etiology of sepsis is notwell characterized at this time. Further, there is no known biomarkerunique to sepsis. Clinicians may rely on non-specific indicators such asfever, white blood cell counts (WBC), and altered mental state (AMS) toidentify patients who might have sepsis. These tests are non-specific inthat they are present in a variety of conditions other than sepsis,including some cases of non-septic infections, trauma, burns, cancers,etc. Diagnostic tests, including tests for procalcitonin (PCT) andC-Reactive Protein (CRP) are available, but are not desirably specificor sensitive to sepsis. That is, available diagnostic tests both testpositive for patients who are not septic and test negative for patientswho are septic or who are developing sepsis, at undesirably high rates.

There remains a need for diagnostic tests which can help a cliniciandistinguish sepsis from other conditions, including flu, trauma, cancer,and non-septic inflammation.

BRIEF SUMMARY

In some aspects, this disclosure relates to a system for evaluatingvariation in a cell population parameter. The system may comprise aflowcell with a flow of a liquid containing a plurality of cells throughthe flowcell. The system may comprise a light source, and one or moresensors for detecting light scatter as cells pass through the flowcell.The one or more sensors may comprise a sensor for detecting upper medianangle light scatter (UMALS). The system may comprise a processor foridentifying non-nucleated red blood cells (NNRBC) based at least in parton one or more light scatter measurements. The processor may furthercollect the UMALS sensor measurement or measurements for a plurality ofidentified NNRBCs. The processor may calculate a standard deviation forthe UMALS measurements for the plurality of identified NNRBCs.

According to a first aspect, some embodiments may provide a method forcharacterizing an inflammatory response to infection. In someembodiments, such a method may comprise flowing a body fluid samplethough a flowcell, and irradiating a plurality of cells in the bodyfluid sample in the flowcell. In some embodiments, such a method mayfurther comprise measuring light scatter from individual cells of theplurality of cells including at least UMALS light scatter. In someembodiments, such a method may further comprise identifying individualcells within the plurality of cells, based at least in part on the lightscatter measurements. In some embodiments, such a method may furthercomprise analyzing the UMALS light scatter measurements as a cellpopulation parameter.

According to a second aspect, in some embodiments such as described inthe context of the first aspect the body fluid sample may be wholeblood.

According to a third aspect, in some embodiments such as described inthe context of any of the first or second aspects, the cell populationparameter may be analyzed for cells from the plurality of cellsclassified as NNRBC.

According to a fourth aspect, in some embodiments such as described inthe context of any of the first through third aspects, the cellpopulation parameter may be NNRBC-UMALS-SD.

According to a fifth aspect, in some embodiments such as described inthe context of the fourth aspect, the method may comprise comparing theNNRBC-UMALS-SD parameter to an NNRBC-UMALS-SD reference range, whereinthe inflammatory response to infection is characterized as abnormal ifthe NNRBC-UMALS-SD is outside the NNRBC-UMALS-SD reference range.

According to a sixth aspect, in some embodiments such as described inthe context of any of the fourth or fifth aspects, the inflammatoryresponse to infection may be characterized as abnormal if theNNRBC-UMALS-SD is less than 43.

According to a seventh aspect, in some embodiments such as described inthe context of any of the fourth through sixth aspects, the method mayfurther comprise determining whether the distribution width of measuredvolumes for a population of monocytes (MDW) within the body fluid sampleis within an MDW reference range.

According to an eighth aspect, in some embodiments such as described inthe context of the seventh aspect, the method may comprisecharacterizing the inflammatory response to infection as abnormal if theNNRBC-UMALS-SD is outside the NNRBC-UMALS-SD reference range and the MDWis outside the MDW reference range.

According to a ninth aspect, in some embodiments such as described inthe context of the seventh or eighth aspects, the inflammatory responseto infection is characterized as abnormal if the NNRBC-UMALS-SD is lessthan 43 and the distribution width of the volume of monocytes is greaterthan 19 channels.

According to a tenth aspect, in some embodiments such as described inthe context of any of the fourth through eighth aspects, the method mayfurther comprise determining whether a count of white blood cells (WBC)in the body fluid sample is within a normal reference range.

According to an eleventh aspect, in some embodiments such as describedin the context of the tenth aspect, the inflammatory response toinfection may be characterized as abnormal if the NNRBC-UMALS-SD is lessthan 43 and the WBC is less than 4,000 cells/mm³ or greater than 12,000cells/mm³.

According to a twelfth aspect, in some embodiments such as described inthe context of the first aspect, the method may comprise determiningNNRBC-UMALS-SD, MDW and WBC. In some such embodiments, the method mayfurther comprise comparing each of the NNRBC-UMALS-SD, the MDW, and theWBC to a respective reference range. In some such embodiments, themethod may further comprise characterizing the inflammatory response toinfection based on a combination of at least the NNRBC-UMALS-SD, theMDW, and the WBC.

According to a thirteenth aspect, in some embodiments such as describedin the context of the twelfth aspect, the inflammatory response toinfection may be characterized as abnormal if the NNRBC-UMALS-SD isoutside the NNRBC-UMALS-SD reference range, the MDW is outside the MDWreference range, and the WBC is outside the WBC reference range.

According to a fourteenth aspect, in some embodiments such as describedin the context of any of the twelfth to thirteenth aspects, localdecision rules may be applied to characterize the inflammatory responseto infection if the NNRBC-UMALS-SD, the MDW and the WBC are not allwithin or all outside of their respective reference ranges.

According to a fifteenth aspect, in some embodiments a system may beprovided that comprises a transducer module for measuring at least UMALSlight scatter caused by cells passing through the flowcell of the methoddescribed in the context of any of the first through fourteenth aspects.In some such embodiments, the system may comprise a processor configuredwith instructions on a non-transitory computer readable medium forperforming the method described in the context of any of the firstthrough fourteenth aspects.

According to a sixteenth aspect, in some embodiments such as describedin the context of the fifteenth aspect, the transducer module maycomprise means for measuring RF conductivity wherein the means formeasuring RF conductivity is operable to measure RF conductivity ofcells passing through the flowcell of the method of any of the firstthrough fourteenth aspects and is also operable to measure RFconductivity of cells passing through a second flowcell comprised by thetransducer module.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of an exemplary cellular analysis process inaccordance with aspects of this disclosure.

FIG. 2 is a schematic depiction of an exemplary cellular analysis systemin accordance with aspects of this disclosure.

FIG. 3 is an illustration of an exemplary transducer module andassociated components in accordance with aspects of this disclosure.

FIG. 4 is a simplified block diagram of an exemplary module system inaccordance with aspects of this disclosure.

FIG. 5 is a Box Plot of NNRBC UMALS standard deviation by patientcondition from an exemplary patient data set in accordance with aspectsof this disclosure.

FIG. 6 is a ROC Curve with plots of sensitivity versus specificity forsepsis for NNRBC UMALS SD, MDW, and a combination of NNRBC UMALS SD andMDW in accordance with aspects of this disclosure.

FIG. 7 is a flowchart of exemplary possible algorithms in accordancewith aspects of this disclosure.

DETAILED DESCRIPTION

Prior efforts to provide an objective, diagnostic test for sepsis haveincluded hematological cellular analysis. Abnormal White Blood CellCounts (WBC) are often associated with infection, although they are notspecific to sepsis. More recently, evaluation of other cell populations,such as immature granulocytes, has been proposed as a factor to considerin evaluating the likelihood that a patient has or is developing sepsis.Other proposals have included looking at cell population parameters,such as monocyte volume distribution width or neutrophil volumedistribution width, as potential indicators of sepsis. Thesehematological approaches have tended to focus on white blood cells,including monocytes, lymphocytes, and neutrophils, which are known to beinvolved in the immune response to infection.

Surprisingly, a UMALS light scatter measurement of non-nucleated redblood cells (NNRBC) has been found to be a good predictor of sepsis,either alone or in combination with other blood cell populationparameters. In particular, the standard deviation of the UMALS lightscatter measurement for a population of NNRBC may be a good predictor ofsepsis. UMALS is sometimes associated with cell granularity, so forgranulocytes and their progenitors, UMALS would, in hindsight, seem tobe a cellular characteristic of reasonable interest. However, NNRBC donot granulate in response to infection, and are typically a heterogenouspopulation due to changes in red blood cell size and morphology as cellsage. There was no apparent reason to believe that UMALS measurement ofNNRBC would provide insight into the likelihood a patient has or is atrisk of developing sepsis, and in particular no reason to believe thatchanges in the standard deviation for the UMALS measurement of NNRBCwould help identify patients with sepsis or developing sepsis.

A blood sample from a patient can be analyzed manually, e.g., bysmearing blood on a slide and visually examining the slide. A manualoperator can make counts of cells and identify cells by type, e.g., redblood cells, platelets, white blood cells, possibly by using visual aidsto facilitate counting and/or sizing cells on the slide. However, it maybe desirable to automate the analysis of a blood sample from a patient.Beyond the convenience of having an automated process, an automated orsemi-automated cellular analysis system may be able, for example, tocount a vastly larger number of cells in a blood sample, or to gatherinformation about individual cells and/or cell populations that would beextremely challenging or impossible for a human to collect at acomparable sample size. These abilities are important to producingsufficient data points for cell population statistics, such asdistribution width, that are robust based on sample size.

Cellular analysis systems may use a variety of techniques to identify,count and/or characterize cells. For example, a cellular analysis systemmay use electrical impedance to determine the volume and quantity ofcells passing through an interrogation zone in a flowcell. As anotherexample, a cellular analysis system may use imaging technology tocapture optical representations of the cell and analyze the opticalrepresentations (which might or might not be human-comprehensible oramenable to conversion to human-comprehensible images) to determine thesize and quantity of cells in an interrogation zone, either in aquiescent system or in a flowcell. As yet another example, a cellularanalysis system may use flow cytometry to irradiate cells passingthrough a flowcell and measure the transmission and/or scatter of thelight as it passes through the cell. The light scatter may inherentlydistinguish different cells of different kinds, sizes orcharacteristics, or the cells may be prepared with markers, such asfluorescent markers, to facilitate the identification, quantificationand/or characterization of the cells based on cellular featuresmarked—or unmarked—by the marker. A cellular analysis system may usecombinations of these and/or other techniques to count, identify, and/orcharacterize cells. For example, a cellular analysis system may use acombination of electrical impedance and light scatter to analyze cellsin a blood sample. If a combination of techniques is used, thetechniques may employ hardware set up in serial progression (e.g., thesame sample or aliquot of a sample is passed through multiple, separateinterrogation zones), or in parallel progression (e.g., differentaliquots of a sample are passed at essentially the same time throughmultiple, separate interrogation zones), or two or more techniques maybe employed at the essentially the same time (e.g., a flow cell may beequipped to measure both electrical impedance and light scatter from thesame sample or aliquot of sample in the same flow cell at essentiallythe same time). In this regard, essentially the same time means that theprocesses are running in overlapping time intervals for the same sampleor different aliquots of the same sample. It is not essential to thepractice of the invention that the different techniques be coordinatedto occur at precisely the same time or in time intervals of equalduration.

A sample for analysis may be any biological fluid which contains cells.The biological fluid may, for example, be blood. The sample may be wholeblood, e.g., blood which has not been processed or modified except forthe possible addition of an anticoagulant to prevent the blood fromclotting, which would complicate flowing the blood through a flowcellfor analysis. The sample may be processed, e.g., by dilution, byconcentration, by separation into components (such as plasma, serum, andcells); by pretreatment (e.g., with cytometry markers, with a lyse torupture/remove certain cell types, with a stain to modify the appearanceof one or more cells, etc.), with a sphering agent, or otherwise as maybe helpful to prepare the sample for analysis. The blood may be humanblood or non-human animal blood. In some circumstances, the sample maybe from a non-blood body fluid, such as urine, synovial fluid, saliva,bile, cerebrospinal fluid, amniotic fluid, semen, mucus, sputum, lymph,aqueous humour, tears, vaginal secretions, pleural fluid, pericardialfluid, peritoneal fluid, and the like. As with blood, if non-blood bodyfluids are sampled, the non-blood body fluids may be processed, e.g.,concentrated or the cells otherwise enriched, as by centrifugation, toachieve a desirable cellular concentration or to enrich or modifycertain sub-populations of cells for analysis. A possible advantage ofevaluating whole blood may be the relatively large number of cellsavailable for analysis in a relatively small sample. A possibleadvantage of analyzing non-blood body fluids and/or processed blood maybe pre-segregation of certain cells of interest and/or a reduction inthe number of cells, because of differences in the types and number ofcells that normally occur in different body fluids. A lower number ofcells may be helpful, for example, for characterizing individual cells.

In some aspects, cells passing through a flowcell are analyzed usinglight scatter. As shown in FIG. 1 , a method 100 for evaluating cellpopulation variations may comprise flowing a sample through a flowcell110. Cells within the sample flowing through the flowcell may beirradiated 120, as with visible light. The cellular analysis system maycomprise one or more sensors which allow the analyzer to measure lighttransmission and/or scatter 130 as a cell is irradiated in the flowcell.The cellular analysis system may comprise a processor or means forcommunicating with a remote processor to collect the light transmissionand/or scatter for a plurality of cells in the sample 140 as the cellsflow through the flowcell. The processor may use an algorithm toidentify cells based, at least in part, on light transmission and/orscatter 150. The processor, or a separate processor, may analyze thelight transmission and/or scatter data for a particular cell or for aparticular cell type population 160 (e.g., monocytes, neutrophils, redblood cells). The analysis could comprise, for example, calculatingparameters, such as extrema, ranges, standard deviations, distributionwidths, etc. for a particular measure, such as cell volume or lightscatter, and/or for a particular cell type, such as monocytes,neutrophils, or NNRBC. For example, the cellular analysis system maycalculate the standard deviation of light scatter, or of a particularangle of light scatter, such as UMALS, for cells identified as NNRBC. Insome aspects, measurement 130 could involve alternative measurements ofcell size and/or granularity, such as image analysis, electricalimpedance, radiofrequency (RF) response, flow cytometry with or withoutmarkers, alone or in combination or sub-combinations, and with orwithout light transmission and/or light scatter measures.

FIG. 2 schematically depicts a cellular analysis system 200. As shownhere, system 200 includes a preparation system 210, a transducer module220, and an analysis system 230. While system 200 is described generallywith reference to three core system blocks (210, 220, and 230), theskilled artisan readily understands that system 200 may include othersystem components such as central control processor(s), displaysystem(s), fluidic system(s), temperature control system(s), user-safetycontrol system(s), and the like. In operation, a whole blood sample(WBS) 240 can be presented to the system 200 for analysis. In someinstances, WBS 240 is aspirated into system 200. Exemplary aspirationtechniques are known to the skilled artisan. After aspiration, WBS 240can be delivered to a preparation system 210. Preparation system 210receives WBS 240 and can perform operations involved with preparing WBS240 for further measurement and analysis. For example, preparationsystem 210 may separate WBS 240 into one or more predefined aliquots forpresentation to transducer module 220. In some aspects, preparationsystem 210 may make no changes to the composition of WBS 240.Alternately, preparation system 210 may include mixing chambers so thatappropriate reagents may be added to one or more of the aliquots. Forexample, where an aliquot is to be tested for differentiation of whiteblood cell subset populations, a lysing reagent (e.g. ERYTHROLYSE, a redblood cell lysing buffer) may be added to the aliquot to break up andremove the RBCs. Preparation system 210 may also include temperaturecontrol components to control the temperature of the reagents and/ormixing chambers. Appropriate temperature controls can improve theconsistency of the operations of preparation system 210, and mayfacilitate pre-treatment of cells in the sample, e.g., with fluorescentmarkers, stains, or lyse.

In some instances, one or more predefined aliquots can be transferredfrom preparation system 210 to transducer module 220. As described infurther detail below, transducer module 220 can perform lighttransmission, and/or light scatter measurements of cells from the WBSpassing individually therethrough. Measured light propagation (e.g.,light transmission, light scatter) parameters can be provided ortransmitted to analysis system 230 for data processing. In someinstances, analysis system 230 may include computer processing featuresand/or one or more modules or components such as those described hereinwith reference to the system depicted in FIG. 4 and described furtherbelow, which can evaluate the measured parameters, identify andenumerate at least one of the blood cellular constituents, and calculatecell population parameters for one or more cell populations within thealiquot. As shown here, cellular analysis system 200 may generate oroutput a report 250 containing measurements and/or calculated parametersfor one or more cell populations within the aliquot, e.g., monocytevolume distribution width, neutrophil volume distribution width, a countor percentage of immature granulocytes, and/or a standard deviation of aUMALS measurement for NNRBC. In some instances, excess biological samplefrom transducer module 220 can be directed to an external (oralternatively internal) waste system 260. An exemplary cellular analysissystem is a Beckman Coulter DxH hematology analyzer, which measuresdirect current impedance to determine cell volume, conductivity, andlight scatter, for cytoplasmic granularity and nuclear structure.

Because there is no known biomarker specific to sepsis (e.g., in thesense that identifying a malarial parasite in a blood cell definitivelyindicates a malarial infection), there is currently no hematologicalanalysis which can definitively diagnosis sepsis. However, identifying,enumerating and/or characterizing one or more cell populations in apatient samples may provide information which, in combination withclinical signs and symptoms and potentially with other tests orcharacterization studies, can reliably increase or decrease a clinicalsuspicion of sepsis or developing sepsis. Notably, because sepsis is asyndrome defined based on clinical symptoms, and because cell populationchanges may be observed before the clinical symptoms of sepsis, cellpopulation data may help identify patients at high risk of developingsepsis, allowing for prophylactic treatment. This is advantageousbecause prophylactic treatment often involves the administration ofantibiotic, antiviral and/or antifungal medications that posechallenges. For example, overuse of antibiotics in patients who are notseptic or developing sepsis can contribute to the development ofantimicrobial resistance. Further, some medications may have sideeffects or trigger adverse events that can be dangerous for a patientwho is seriously ill or whose clinical state is declining. Accordingly,a test which can help a clinician develop an informed clinical treatmentplan is valuable even if the test itself is not definitively diagnostic.Further, characterizing and/or enumerating cell populations that changeduring or in advance of a patient developing sepsis may be useful fornon-diagnostic purposes, such as research into the etiology orprogression of sepsis, or observing cellular responses to infection.

In some aspects, the analysis of a patient sample may cause a clinicianto initiate and/or modify a treatment regimen. Treatment regimens mayinvolve administration of one or more medications or therapeutic agentsto an individual for the purposes of addressing the patient's condition.Any of a variety of therapeutic modalities can be used for treating anindividual identified as having an abnormal NNRBC UMALS standarddeviation as discussed herein. Exemplary therapies may include theadministration of fluids, vasopressors, antibiotics, antifungals,antivirals, vitamins (including thiamine), minerals, steroids (includingcorticosteroids), and combinations thereof. In some instances, a patientmay be subjected to more or less rigorous monitoring, including beingadmitting to a hospital for professional observation, based on theanalysis of the patient sample. As used herein, an NNRBC UMALS standarddeviation is considered normal if it is not associated with adysfunctional immune response to infection and/or sepsis. An NNRBC UMALSstandard deviation is considered abnormal if it is associated with adysfunctional immune response to infection and/or sepsis.

FIG. 3 illustrates in more detail a transducer module and associatedcomponents in more detail. As shown here, system 300 includes atransducer module 310 having a light or irradiation source such as alaser 312 emitting a beam 314. The laser 312 can be, for example, a 635nm, 5 mW, solid-state laser. In some instances, system 300 may include afocus-alignment system 320 that adjusts beam 314 such that a resultingbeam 322 is focused and positioned at a cell interrogation zone 332 of aflow cell 330. In some instances, flow cell 330 receives a samplealiquot from a preparation system 302. Various fluidic mechanisms andtechniques can be employed for hydrodynamic focusing of the samplealiquot within flow cell 330.

In some instances, the aliquot generally flows through the cellinterrogation zone 332 such that its constituents pass through the cellinterrogation zone 332 one at a time. In some cases, a system 300 mayinclude a cell interrogation zone or other feature of a transducermodule or blood analysis instrument such as those described in U.S. Pat.Nos. 5,125,737; 6,228,652; 7,390,662; 8,094,299; and 8,189,187, thecontents of which are incorporated herein by references. For example, acell interrogation zone 332 may be defined by a square transversecross-section measuring approximately 50×50 microns, and having a length(measured in the direction of flow) of approximately 65 microns. Flowcell 330 may include an electrode assembly having first and secondelectrodes 334, 336 for performing DC impedance and/or RF conductivitymeasurements of the cells passing through cell interrogation zone 332.Signals from electrodes 334, 336 can be transmitted to analysis system304. The electrode assembly can analyze volume and conductivitycharacteristics of the cells using low-frequency current andhigh-frequency current, respectively. For example, low-frequency DCimpedance measurements can be used to analyze the volume of eachindividual cell passing through the cell interrogation zone.High-frequency RF current measurements can be used to determine theconductivity of cells passing through the cell interrogation zone.Because cell walls act as conductors to high frequency current, the highfrequency current can be used to detect differences in the insulatingproperties of the cell components, as the current passes through thecell walls and through each cell interior. High frequency current can beused to characterize nuclear and granular constituents and the chemicalcomposition of the cell interior.

The light source in FIG. 3 has been described as a laser, however, thelight source may alternatively or additionally include a xenon lamp, anLED lamp, an incandescent lamp, or any other suitable source of light,including combinations of the same or different kinds of lamps (e.g.,multiple LED lamps or at least one LED lamp and at least one xenonlamp). As shown in FIG. 3 , for example, incoming beam 322 irradiatesthe cells passing through cell interrogation zone 332, resulting inlight propagation within an angular range a (e.g. scatter, transmission)emanating from the zone 332. Exemplary systems are equipped with sensorassemblies that can detect light within one, two, three, four, five, ormore angular ranges within the angular range a, including lightassociated with an extinction or axial light loss measure. As shown,light propagation 340 can be detected by a light detection assembly 350,optionally having a light scatter detector unit 350A and a light scatterand/or transmission detector unit 350B. In some instances, light scatterdetector unit 350A includes a photoactive region or sensor zone fordetecting and measuring upper median angle light scatter (UMALS), forexample light that is scattered or otherwise propagated at anglesrelative to a light beam axis within a range from about 20 to about 42degrees. In some instances, UMALS corresponds to light propagated withinan angular range from between about 20 to about 43 degrees, relative tothe incoming beam axis which irradiates cells flowing through theinterrogation zone. Light scatter detector unit 350A may also include aphotoactive region or sensor zone for detecting and measuring lowermedian angle light scatter (LMALS), for example light that is scatteredor otherwise propagated at angles relative to a light beam axis within arange from about 10 to about 20 degrees. In some instances, LMALScorresponds to light propagated within an angular range from betweenabout 9 to about 19 degrees, relative to the incoming beam axis whichirradiates cells flowing through the interrogation zone.

A combination of UMALS and LMALS is defined as median angle lightscatter (MALS), which may be light scatter or propagation at anglesbetween about 9 degrees and about 43 degrees relative to the incomingbeam axis which irradiates cells flowing through the interrogation zone.One of skill in the art will understand that these angles (and the otherangles described herein) may vary somewhat based on the configuration ofthe interrogation, sensing and analysis systems.

As shown in FIG. 3 , the light scatter detector unit 350A may include anopening 351 that allows low angle light scatter or propagation 340 topass beyond light scatter detector unit 350A and thereby reach and bedetected by light scatter and transmission detector unit 350B. Accordingto some embodiments, light scatter and transmission detector unit 350Bmay include a photoactive region or sensor zone for detecting andmeasuring lower angle light scatter (LALS), for example light that isscattered or propagated at angles relative to an irradiating light beamaxis of less than about 5.1 degrees. In some instances, LALS correspondsto light propagated at an angle of less than about 9 degrees, relativeto the incoming beam axis which irradiates cells flowing through theinterrogation zone. In some instances, LALS corresponds to lightpropagated at an angle of less than about 10 degrees, relative to theincoming beam axis which irradiates cells flowing through theinterrogation zone. In some instances, LALS corresponds to lightpropagated at an angle of about 1.9 degrees±0.5 degrees, relative to theincoming beam axis which irradiates cells flowing through theinterrogation zone. In some instances, LALS corresponds to lightpropagated at an angle of about 3.0 degrees±0.5 degrees, relative to theincoming beam axis which irradiates cells flowing through theinterrogation zone. In some instances, LALS corresponds to lightpropagated at an angle of about 3.7 degrees±0.5 degrees, relative to theincoming beam axis which irradiates cells flowing through theinterrogation zone. In some instances, LALS corresponds to lightpropagated at an angle of about 5.1 degrees±0.5 degrees, relative to theincoming beam axis which irradiates cells flowing through theinterrogation zone. In some instances, LALS corresponds to lightpropagated at an angle of about 7.0 degrees±0.5 degrees, relative to theincoming beam axis which irradiates cells flowing through theinterrogation zone. In each instance, LALS may correspond to lightpropagated an angle of about 1.0 degrees or more. That is, LALs maycorrespond to light propagated at angles between about 1.0 degrees andabout 1.9 degrees; between about 1.0 degrees and about 3.0 degrees;between about 1.0 degrees and about 3.7 degrees; between about 1.0degrees and about 5.1 degrees, between about 1.0 degrees and about 7.0degrees, between about 1.0 degrees and about 9.0 degrees; or betweenabout 1.0 degrees and about 10.0 degrees.

According to some embodiments, light scatter and transmission detectorunit 350B may include a photoactive region or sensor zone for detectingand measuring light transmitted axially through the cells, or propagatedfrom the irradiated cells, at an angle of about 0 degrees relative tothe incoming light beam axis. In some cases, the photoactive region orsensor zone may detect and measure light propagated axially from cellsat angles of less than about 1 degree relative to the incoming lightbeam axis. In some cases, the photoactive region or sensor zone maydetect and measure light propagated axially from cells at angles of lessthan about 0.5 degrees relative to the incoming light beam axis less.Such axially transmitted or propagated light measurements correspond toaxial light loss (ALL or AL2). As noted in previously incorporated U.S.Pat. No. 7,390,662, when light interacts with a particle, some of theincident light changes direction through the scattering process (i.e.light scatter) and part of the light is absorbed by the particles. Bothof these processes remove energy from the incident beam. When viewedalong the incident axis of the beam, the light loss can be referred toas forward extinction or axial light loss. Additional aspects of axiallight loss measurement techniques are described in U.S. Pat. No.7,390,662 at column 5, line 58 to column 6, line 4.

As such, the cellular analysis system 300 provides means for obtaininglight propagation measurements, including light scatter and/or lighttransmission, for light emanating from the irradiated cells of thebiological sample at any of a variety of angles or within any of avariety of angular ranges, including ALL and multiple distinct lightscatter or propagation angles. For example, light detection assembly350, including appropriate circuitry and/or processing units, provides ameans for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL.

Wires or other transmission or connectivity mechanisms can transmitsignals from the electrode assembly (e.g. electrodes 334, 336), lightscatter detector unit 350A, and/or light scatter and transmissiondetector unit 350B to analysis system 304 for processing. For example,measured DC impedance, RF conductivity, light transmission, and/or lightscatter parameters can be provided or transmitted to analysis system 304for data processing. In some instances, analysis system 304 may includecomputer processing features and/or one or more modules or componentssuch as those described herein with reference to the system depicted inFIG. 4 , which can evaluate the measured parameters, identify andenumerate biological sample constituents, and correlate a subset of datacharacterizing elements of the biological sample with one or morefeatures or parameters of interest. As shown here, cellular analysissystem 300 may generate or output a report 306 presenting measurementsmade or parameters calculated for the sample, such as WBC, MDW, or UMALSstandard deviation for NNRBC. In some instances, excess biologicalsample from transducer module 310 can be directed to an external (oralternatively internal) waste system 308. In some instances, a cellularanalysis system 300 may include one or more features of a transducermodule or blood analysis instrument such as those described inpreviously incorporated U.S. Pat. Nos. 5,125,737; 6,228,652; 8,094,299;and 8,189,187.

FIG. 4 is a simplified block diagram of an exemplary module system thatbroadly illustrates how individual system elements for a module system600 may be implemented in a separated or more integrated manner. Modulesystem 600 may be part of or in connectivity with a cellular analysissystem. Module system 600 is well suited for producing data or receivinginput related to a cellular analysis. In some instances, module system600 includes hardware elements that are electrically coupled via a bussubsystem 602, including one or more processors 604, one or more inputdevices 606 such as user interface input devices, and/or one or moreoutput devices 608 such as user interface output devices. In someinstances, system 600 includes a network interface 610, and/or adiagnostic system interface 640 that can receive signals from and/ortransmit signals to a diagnostic system 642. In some instances, system600 includes software elements, for example shown here as beingcurrently located within a working memory 612 of a memory 614, anoperating system 616, and/or other code 618, such as a programconfigured to implement one or more aspects of the techniques disclosedherein. Memory 614 may be non-transitory and/or embodied in tangiblemedia, such as hardware.

In some embodiments, module system 600 may include a storage subsystem620 that can store the basic programming and data constructs thatprovide the functionality of the various techniques disclosed herein.For example, software modules implementing the functionality of methodaspects, as described herein, may be stored in storage subsystem 620.These software modules may be executed by the one or more processors604. In a distributed environment, the software modules may be stored ona plurality of computer systems and executed by processors of theplurality of computer systems. Storage subsystem 620 can include memorysubsystem 622 and file storage subsystem 628. Memory subsystem 622 mayinclude a number of memories including a main random access memory (RAM)626 for storage of instructions and data during program execution and aread only memory (ROM) 624 in which fixed instructions are stored. Filestorage subsystem 628 can provide persistent (non-volatile) storage forprogram and data files, and may include tangible storage media which mayoptionally embody patient, treatment, assessment, or other data. Filestorage subsystem 628 may include a hard disk drive, a floppy disk drivealong with associated removable media, a Compact Digital Read OnlyMemory (CD-ROM) drive, an optical drive, DVD, CD-R, CD RW, solid-stateremovable memory, other removable media cartridges or disks, and thelike. One or more or all of the drives may be located at remotelocations on other connected computers at other sites coupled to modulesystem 600. In some instances, systems may include a computer-readablestorage medium or other tangible storage medium that stores one or moresequences of instructions which, when executed by one or moreprocessors, can cause the one or more processors to perform any aspectof the techniques or methods disclosed herein. One or more modulesimplementing the functionality of the techniques disclosed herein may bestored by file storage subsystem 628. In some embodiments, the softwareor code will provide protocol to allow the module system 600 tocommunicate with communication network 630. Optionally, suchcommunications may include dial-up or internet connectioncommunications.

System 600 can be configured to carry out various aspects of methods ofthe present disclosure. For example, processor component or module 604can be a microprocessor control module configured to receive cellularparameter signals from a sensor input device or module 632, from a userinterface input device or module 606, and/or from a diagnostic system642, optionally via a diagnostic system interface 640 and/or a networkinterface 610 and a communication network 630. In some instances, sensorinput device(s) may include or be part of a cellular analysis systemthat is equipped to obtain multiple light angle detection parameters,such as in a Beckman Coulter DxH™ hematology analyzer. In someinstances, user interface input device(s) 606 and/or network interface610 may be configured to receive cellular parameter signals generated bya cellular analysis system that is equipped to obtain multiple lightangle detection parameters, such as a Beckman Coulter DxH™ HematologyAnalyzer. In some instances, diagnostic system 642 may include or bepart of a cellular analysis system that is equipped to obtain multiplelight angle detection parameters, such as a Beckman Coulter DxH™Hematology Analyzer.

Processor component or module 604 can also be configured to transmitcellular parameter signals, optionally processed according to any of thetechniques disclosed herein or known to one of skill in the art, tosensor output device or module 636, to user interface output device ormodule 608, to network interface device or module 610, to diagnosticsystem interface 640, or any combination thereof. Each of the devices ormodules according to embodiments of the present disclosure can includeone or more software modules on a computer readable medium that isprocessed by a processor, or hardware modules, or any combinationthereof. Any of a variety of commonly used platforms, such as Windows,MacIntosh, and Unix, along with any of a variety of commonly usedprogramming languages, may be used to implement embodiments of thepresent disclosure.

User interface input devices 606 may include, for example, a touchpad, akeyboard, pointing devices such as a mouse, a trackball, a graphicstablet, a scanner, a joystick, a touchscreen incorporated into adisplay, audio input devices such as voice recognition systems,microphones, and other types of input devices. User input devices 606may also download a computer executable code from a tangible storagemedia or from communication network 630, the code embodying any of themethods or aspects thereof disclosed herein. It will be appreciated thatterminal software may be updated from time to time and downloaded to theterminal as appropriate. In general, use of the term “input device” isintended to include a variety of conventional and proprietary devicesand ways to input information into module system 600.

User interface output devices 606 may include, for example, a displaysubsystem, a printer, a fax machine, or non-visual displays such asaudio output devices. The display subsystem may be a cathode ray tube(CRT), a flat-panel device such as a liquid crystal display (LCD), aprojection device, or the like. The display subsystem may also provide anon-visual display such as via audio output devices. In general, use ofthe term “output device” is intended to include a variety ofconventional and proprietary devices and ways to output information frommodule system 600 to a user. In some instances, a cellular analysissystem may not directly include a user interface output device, insteadtransferring data to a network, computer processor, or computer-readablenon-transitory storage medium, with data display for a human useroccurring in connection with that device or with devices to which thedata from the cellular analysis system is further transferred after theinitial transfer. If data is transferred from the analyzer withoutdisplay, the data transferred may be raw sensor data or processed dataor a combination of raw and processed data.

Bus subsystem 602 provides a mechanism for letting the variouscomponents and subsystems of module system 600 communicate with eachother as intended or desired. The various subsystems and components ofmodule system 600 need not be at the same physical location but may bedistributed at various locations within a distributed network. Althoughbus subsystem 602 is shown schematically as a single bus, alternateembodiments of the bus subsystem may utilize multiple busses.

Network interface 610 can provide an interface to an outside network 630or other devices. Outside communication network 630 can be configured toeffect communications as needed or desired with other systems. It canthus receive an electronic packet from module system 600 and transmitany information as needed or desired back to module system 600. Asdepicted here, communication network 630 and/or diagnostic systeminterface 642 may transmit information to or receive information from adiagnostic system 642 that is equipped to obtain multiple light angledetection parameters, such as such as a Beckman Coulter DxH™ CellularAnalysis System. As non-limiting examples, outside communication network630 may be used to transmit data between a cellular analysis system anda research database, a laboratory information system (LIS), anelectronic medical record (EMR), and the like. In some instances, thecommunication may be one-way, with information flowing from the cellularanalysis system to other systems. In some instances, the communicationmay be one-way with information (such as orders for specificmeasurements to be made or population parameters to be calculated)flowing from an external system, which may be remote or physicallyproximate to the cellular analysis system, to the cellular analysissystem. In some instances, the communication may be two-way. In someinstances, the information communicated to the cellular analysis systemby an external system may include patient information useful inevaluating the significance of cellular measurements. For example, somereference ranges for hematology parameters may differ for pediatricpopulations or specific patient sub-populations relative to a generaladult population, and the cellular analysis system may consider patientinformation when determining whether to flag analytical results forfurther review.

In addition to providing such infrastructure communications linksinternal to the system, the communications network system 630 may alsoprovide a connection to other networks such as the internet and maycomprise a wired, wireless, modem, and/or other type of interfacingconnection.

It will be apparent to the skilled artisan that substantial variationsmay be used in accordance with specific requirements. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), firmware, or combinations thereof. Further, connection toother computing devices such as network input/output devices may beemployed. Module terminal system 600 itself can be of varying typesincluding a computer terminal, a personal computer, a portable computer,a workstation, a network computer, or any other data processing system.Due to the ever-changing nature of computers and networks, thedescription of module system 600 depicted in FIG. 4 is intended only asa specific example for purposes of illustrating one or more embodimentsof the present disclosure. Many other configurations of module system600 are possible having more or less components than the module systemdepicted in FIG. 4 . Any of the modules or components of module system600, or any combinations of such modules or components, can be coupledwith, or integrated into, or otherwise configured to be in connectivitywith, any of the cellular analysis system embodiments disclosed herein.Relatedly, any of the hardware and software components discussed abovecan be integrated with or configured to interface with other medicalassessment or treatment systems used at other locations.

In some embodiments, the module system 600 can be configured to receiveone or more cellular analysis parameters of a patient at an inputmodule. Cellular analysis parameter data can be transmitted to anassessment module where raw sensor data or partially analyzed sensordata is further processed and/or evaluated in conjunction withadditional information, possibly including prior laboratory results forthe same patient; laboratory results from other types of analyzers orlaboratory analyses; non-laboratory data about the patient, such aspatient complaints, diagnostic history, vitals, or physical examinationfindings, or combinations thereof. The cellular analysis, such as WBC,MDW, NNRBC UMALS standard deviation, and other cell populationparameters can be output to a system user via an output module. In somecases, the module system 600 can determine an initial treatment orinduction protocol for the patient, or an adjusted treatment protocol,based on one or more cellular analysis parameters and/or the predictedsepsis status, for example by using a treatment module. The treatmentcan be output to a system user via an output module. Optionally, certainaspects of the treatment can be determined by an output device, andtransmitted to a treatment system or a sub-device of a treatment system.Any of a variety of data related to the patient can be input into themodule system, including age, weight, sex, treatment history, medicalhistory, and the like. Parameters of treatment regimens or diagnosticevaluations can be determined based on such data.

Analysis system 304 of transducer module 300 or other code programs 618of module system 600 or both may comprise one or more algorithms forprocessing sensor data generated by transducer module 300. The one ormore algorithms may process the sensor data to identify and count cells.For example, individual cells may be identifiable based on light scatterand/or light transmission data that provides an indication of the sizeand certain surface properties of the cells. As another example,electrical impedance may provide an indication of the size of the cell.Radiofrequency conductivity may provide an indication of cellularconstituents that may be useful in distinguishing granulocytes andnucleated versus non-nucleated cells. Markers, stains, image analysis orother measurement techniques may be used to identify cells as, forexample, NNRBC, WBC, monocytes, neutrophils, and the like. An algorithmmay count the number of signals consistent with a given cell type duringan interrogation period, which may be defined by time through a flowcellor imaging time, or may be defined by a volume of body fluid examined,or both.

In some instances, the signals produced are continuous or ordinalvalues, and the magnitude or other properties of the signals may befurther analyzed. For example, higher electrical impedance valuestypically indicate a larger cell, and may be useful for identifying aparticular cell. Electrical impedance values may also correlate to cellvolume, and therefore the magnitude of the signals across many cells inthe sample may also convey useful information about that sub-populationof cells. For example, electrical impedance values may help identifymonocytes and distribution features of the volumes for the monocytes asa sub-population of cells may convey information about an immuneresponse to an infection.

FIG. 7 is a flowchart for exemplary algorithms of potential use inpractice of this disclosure. As shown, a single algorithm 700encompasses all of the illustrated acts, however, different algorithmsor even different software could be used, and, in particular, variousacts could be performed by different algorithms or different softwarethat might reside on or be processed by different hardware. Algorithm700 may identify cell types from sensor signals 705. Algorithm 700 maycount NNRBC 710. Algorithm 700 may calculate NNRBC-UMALS-SD 715.Algorithm 700 may compare the calculated NNRBC-UMALS-SD to anNNRBC-UMALS-SD reference range 720. Algorithm 700 may classify theNNRBC-UMALS-SD as normal or abnormal in relation to the NNRBC-UMALS-SDreference range 725.

Algorithm 700 may count monocytes 730. Algorithm 700 may calculate MDW735. Algorithm 700 may compare the calculated MDW to an MDW referencerange 740. Algorithm 700 may classify MDW as normal or abnormal inrelation to the MDW reference range 745.

Algorithm 700 may count white blood cells 750. Algorithm 700 may comparethe white blood cell count to a WBC reference range 755. Algorithm 700may classify the WBC as normal or abnormal in relation to the WBCreference range 760.

Algorithm 700 may determine whether NNRBC-UMALS-SD, MDW and WBC are allnormal in relation to their respective reference ranges 765. If yes, forthis purpose the results are normal 770, and the patient is notidentified as septic 775. If one or more of NNRBC-UMALS-SD, MDW and WBCare abnormal in relation to their respective reference ranges, then forthis purpose the results are abnormal 780, and the algorithm may apply aglobal or local decision rules 785. A global decision rule is athreshold applied uniformly to all data processed by algorithm 700. Incontrast, local decision rules may be permitted to allow differentinstitutions or different practitioners to establish different rules foridentifying a patient as having an abnormal immune response toinfection, or for identifying a patient as being septic or at elevatedrisk of developing sepsis. Local decision rules allow institutions toadapt the specificity (ability to inclusively identify most or all casesof possible sepsis) and sensitivity (ability to exclude most or callnon-sepsis cases) of the algorithm, to reduce false negatives or falsepositives, respectively. In most or all cases, it is contemplated thatif all of NNRBC-UMALS-SD, MDW and WBC are abnormal, the results would beflagged as abnormal, and, if the results are used to identify sepsis,the patient would be identified as having sepsis or an elevated risk ofdeveloping sepsis. If the results are not all-normal or all-abnormal,the decision rules would apply either outcome A or outcome B in each rowof the table below.

NNRBC- Identify results UMALS-SD MDW WBC overall as abnormal? Normal Notconsidered Not considered No Normal Not considered Normal No NormalNormal Not considered No Normal Normal Normal No Normal Not consideredAbnormal A - No B - Yes Normal Abnormal Not considered A - No B - YesAbnormal Not considered Not considered Yes Abnormal Not consideredAbnormal Yes Abnormal Abnormal Not considered Yes Abnormal Normal Notconsidered A - No B - Yes Abnormal Not considered Normal A - No B - YesAbnormal Normal Normal A - No B - Yes Abnormal Abnormal Abnormal Yes

If the overall results are identified by algorithm 700 as abnormal, thismay be presented as a separate analytical result (e.g., SepsisIndicated? Yes/No), or may be presented as a flag to invite review bylaboratory personnel and/or a clinician (e.g., text or other symbols orindicators in a report indicating that results indicate abnormal immuneresponse to infection and/or indicate possible sepsis). Of course, insome instances, algorithm 700 may not apply any decision rules,deferring to laboratory and/or clinical personnel to interpret theresults of the cellular analysis.

As noted above, a relatively new use of cellular analysis is theevaluation of the likelihood that a patient has or is at elevated risk(relative to a healthy person of similar age) of developing sepsis inthe near-term (1 week or less). Such evaluation has so far centered onwhite blood cells or sub-populations of white blood cells, such asmonocytes or immature granulocytes. However, the inventors havesurprisingly found that the standard deviation of UMALS measurements forNNRBC (NNRBC-UMALS-SD) may also be predictive of sepsis. As shown inFIG. 5 , NNRBC-UMALS-SD is markedly different in sepsis patientscompared to healthy patients, and is distinguishable from patients whohave Systemic Inflammatory Response Syndrome (SIRS) or Infection but notsepsis.

FIG. 6 is an AUC-ROC curve, showing the relationships between MDW,NNRBC-UMALS-SD, and a model in which MDW and NNRBC-UMALS-SD are eachcompared to a threshold value for evaluating a patient's sepsis status.Based on this data set, as described further below, the model combiningMDW and NNRBC-UMALS-SD should be able to correctly distinguish sepsisfrom other conditions approximately 83% of the time. This analysis, aswith FIG. 5 , comes from data collected in a pivotal clinical trialinvolving adult patients, 18-89 yrs., with complete blood count withdifferential performed upon presentation to the ED, and who remainedhospitalized for at least 12 hours. A total of 2,158 subjects wereenrolled and categorized per Sepsis-2 criteria: controls (n=1,088),systemic inflammatory response syndrome (SIRS) (n=441), infection(n=244), sepsis (n=385); and Sepsis-3 criteria: control (n=1,529),infection (n=386), sepsis (n=243).

The best of the inventors' knowledge, no prior study has looked at lightscatter parameter changes for a heterogeneous cell population ofcirculating cells from a septic population, such as NNRBC, compared tocontrols. Prior studies have used hematological analyzers to look atlight scatter changes in specific cell types during sepsis such as inspecific lymphocyte, monocyte, or neutrophil cell populations (reviewedin Zonneveld R, Molema G, Plotz FB: Analyzing neutrophil morphology,mechanics, and motility in sepsis: options and challenges for novelbedside technologies. Crit Care Med 2016; 44: 218-228). With regard tocell surface granulation, no hypothesis-driven study has demonstrated aspecific correlation between sepsis and cell surface granulation in anycell type.

It is well documented that sepsis causes a number of changes incirculating cells. Without wishing to be bound by theory, changes inmembrane protein and lipid composition, changes in Na/Cl pumpconcentration, changes in ratios of cell types, and changes in theactivation state of immune cells could be an underlying cause of acellular granularity change that could impact light scattering. Any ofthese underlying biological mechanisms or combinations thereof coulddrive the observed light scatter differences in the NNRBC parameter.Nonetheless, obtaining the light scatter measurement and calculatingparticular cell population parameters, such as NNRBC-UMALS-SD, involveprocesses that would not occur in nature. The inventors have noindication that human-conducted visual examination of NNRBC granularity,e.g., via review of blood smear slides, would be useful indistinguishing septic and non-septic patients. Granularity can beassessed via blood smear review, but it is subjective and notstandardized. Standard deviations cannot be visually assessed. In thisstudy, mean NNRBC UMALS measurement was not as effective in identifyingseptic patients as NNRBC-UMALS-SD, suggesting that a human impression orestimate of granularity across a relatively small sample of cells wouldbe unreliable for this purpose.

In some instances, an NNRBC-UMALS-SD reference range may be equal to orgreater than 45, or equal to or greater than 43, or equal to or greaterthan 41.74, or equal to or greater than 41.5, or equal to or greaterthan 40, where values below these thresholds may be considered abnormal,associated with abnormal immune response to infection, and/or indicativeof sepsis or elevated risk of sepsis. In some instances, NNRBC-UMALS-SDmay be considered alongside one or more other parameters, such as WBC orMDW. For MDW, a value above 19, or above 20, or above 21, may indicatedthat a patient is septic or at elevated risk of developing sepsis. MDWmay be measured in “channels” based on the signals collected todetermine the volume of the monocytes. Of course, if monocyte volumedistribution width is measured differently, as by image analysis, boththe units and the normal range may vary. For WBC, a value below 4,000cells/mm³ or above 12,000 cells/mm³ may indicate that a patient isseptic or at elevated risk of developing sepsis. One of skill in the artwill appreciate that these ranges are exemplary, and may vary based onthe specific measurement methodologies used, as well as the specifichardware (e.g., light source, sensing hardware) used to make themeasurements. In some instances, not only the reference range but alsothe unit of measure for these parameters may change based on thetransducer module design used. Using two or more of these three criteriamay increase the sensitivity and/or specificity of the cellular analysisfor sepsis prediction relative to using only one of these threecriteria.

NNRBC-UMALS-SD, alone or in combination with other cellular analyses,may be used in conjunction with current standard of care, includingassessments like qSOFA and physical examination by a clinician (looking,e.g., for fever, altered mental state, tachycardia, tachypnea,hypotension, or other symptoms that may be undetectable or unreliablydetectable from cellular analysis, blood chemistry, immunoassay, orother laboratory tests). Using the “Sepsis-2” consensus definition, thestandard of care would include assessment of the patient for SIRS. Apatient is considered to have SIRS when two or more of the followingcriteria are met: a temperature greater than 38 degrees Celsius (C) orless than 36 degrees C., a heart rate greater than 90 beats per minute(bpm), a respiratory rate greater than 20 breaths per minute(breaths/min), and a white blood cell count (WBC) less than 4,000 permicroliter of blood (4,000/mm3) (leukopenia) or greater than 12,000/mm3(leukocytosis). Under Sepsis-2, a patient is considered septic if thepatient meets a minimum of 2 SIRS criteria plus a persistent infection(bacterial, viral or fungal). Using the “Sepsis-3” consensus definition,the standard of care would include a Sequential Organ Failure Assessment(SOFA) or a Quick SOFA (qSOFA). A qSOFA is scored on a scale from 0-3points, with 1 point for each symptom that tests positively. Thesesymptoms are a respiratory rate of over 22 breaths/min, systolicarterial blood pressure of less than or equal to 100 mmHg, and analtered mental status. It has been determined that patients with a qSOFAscore of at least 2 have a 24% in-hospital mortality rate, and 3% forpatents with a qSOFA score of less than 2. Typically, if the qSOFA scoreis of at least 2, then the patent will be evaluated with the full SOFAtest. The SOFA test is scored on a scale of 0-24 and involves evaluatingspecific organ systems (respiratory, cardiovascular, liver, renal,coagulation, and central nervous system). If the SOFA score is alsogreater than or equal to 2 and has or is suspected of having aninfection, then the patient is considered septic under Sepsis-3. In someaspects NNRBC-UMALS-SD may be used with or without other cellularanalysis parameters, such as WBC and/or MDW, to provide additionalinsight into a patient's sepsis status when the standard of care doesnot result in a clear diagnosis. For example, under the Sepsis-2criteria, an inability to definitively identify an infection, e.g.,using blood culture, or an inability to wait for testing to confirminfection, e.g., if the patient's condition is too tenuous to waitseveral days for blood culture results, NNRBC-UMALS-SD may give aclinician confidence in initiating prophylactic treatment or heightenedpatient monitoring (such as in-patient admission for professionalmonitoring) by affirming other indicators of sepsis or developingsepsis.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue to include at least the variability due to the reproducibility ofmeasurements made using the test methods described herein, orindustry-standard test methods if no test method is expressly disclosed.

Every document cited herein, including any cross referenced or relatedpatent or application, is hereby incorporated herein by reference in itsentirety unless expressly excluded or otherwise limited. The citation ofany document is not an admission that it is prior art with respect toany invention disclosed or claimed herein or that it alone, or in anycombination with any other reference or references, teaches, suggests ordiscloses any such invention. Further, to the extent that any meaning ordefinition of a term in this document conflicts with any meaning ordefinition of the same term in a document incorporated by reference, themeaning or definition assigned to that term in this document shallgovern.

When used in the claims, the phrase “means for detecting whether adifferentially expressed sepsis cell population parameter is present ina heterogenous population of circulating cells and characterizing aninflammatory response to infection based at least in part on thatdetection” should be understood as a means plus function limitation asprovided for in 35 U.S.C. § 112(f), in which the functions “detectingwhether a differentially expressed sepsis cell population parameter ispresent in a heterogenous population of circulating cells” and“characterizing an inflammatory response to infection based at least inpart on that detection” are both recited, in which the correspondingstructure for the first function is a computer configured to performacts as illustrated with reference numbers 710-760 and described in thecorresponding text, and the corresponding structure for the secondfunction is a computer configured to perform acts as illustrated withreference numbers 765-785 and described in the corresponding text.

When used in the claims, the phrase “means for measuring RFconductivity” should be understood as a means plus function limitationas provided for in 35 U.S.C. § 112(f), in which the function ismeasuring RF conductivity, and the corresponding structure is electrodesas illustrated with reference numbers 334 and 336 and described in thecorresponding text.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. A method for characterizing inflammatory responseto infection, the method comprising: a. flowing a body fluid samplethrough a flowcell, the body fluid sample comprising a heterogenouspopulation of circulating cells; and b. detecting whether adifferentially expressed sepsis cell population parameter is present inthe heterogenous population of circulating cells by performing stepscomprising: i. irradiating a plurality of cells in the body fluid samplein the flowcell; ii. obtaining a plurality of measurements, wherein theplurality of measurements comprises, for one or more cells within theplurality of cells, a measurement of a cell granularity parameter; andiii. generating the differentially expressed sepsis cell populationparameter based on the plurality of measurements comprising, for one ormore cells within the plurality of cells, the measurement of the cellgranularity parameter, wherein the differentially expressed sepsis cellpopulation parameter is a standard deviation of the measurements of thecell granularity parameter for the one or more cells within theplurality of cells.
 2. The method of claim 1 wherein the body fluidsample is whole blood.
 3. The method of claim 1 wherein the one or morecells within the plurality of cells are classified as non-nucleated redblood cells.
 4. The method of claim 1 further comprising comparing thestandard deviation of the measurements of the cell granularity parameterfor the one or more cells within the plurality of cells to a referencerange, wherein the inflammatory response to infection is characterizedas abnormal if the standard deviation of the measurements of the cellgranularity parameter for the one or more cells within the plurality ofcells is outside the reference range.
 5. The method of claim 4, wherein:a. the measurements of the cell granularity parameter for the one ormore cells within the plurality of cells are upper median angle lightscatter (UMALS) measurements; b. the one or more cells within theplurality of cells are non-nucleated red blood cells; c. theinflammatory response to infection is characterized as abnormal if thestandard deviation of the measurements of the cell granularity parameterfor the one or more cells within the plurality of cells is less than 43.6. The method of claim 4, further comprising determining whether adistribution width of measured volumes for a population of monocyteswithin the body fluid sample is within a second reference range.
 7. Themethod of claim 6, wherein the inflammatory response to infection ischaracterized as abnormal if the standard deviation of the measurementsof the cell granularity parameter for the one or more cells within theplurality of cells is less than 43 and the distribution width of thevolume of the monocytes (MDW) is greater than 19 channels.
 8. The methodof claim 4 further comprising determining whether a count of white bloodcells in the body fluid sample is within a normal reference range. 9.The method of claim 8, wherein the inflammatory response to infection ischaracterized as abnormal if the standard deviation of the measurementsof the cell granularity parameter for the one or more cells within theplurality of cells is less than 43 and the count of white blood cells isless than 4,000 cells/mm³ or greater than 12,000 cells/mm³.
 10. Themethod of claim 4 wherein the cell granularity parameter is ameasurement indicating how packed a cell is with proteins, organelles,and/or other granules.
 11. The method of claim 1 wherein the cellgranularity parameter is a measurement indicating how packed a cell iswith proteins, organelles, and/or other granules.